{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# 第 4 节　用 Python 模拟抽样\n",
    "## 第 3 章　使用 Python 进行数据分析｜用 Python 动手学统计学"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 1. 环境准备"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "# 用于数值计算的库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "# 用于绘图的库\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "sns.set()\n",
    "\n",
    "# 设置浮点数打印精度\n",
    "%precision 3\n",
    "# 在 Jupyter Notebook 里显示图形\n",
    "%matplotlib inline"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:09.581124Z",
     "end_time": "2024-04-16T13:21:09.590778Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 3. 在只有 5 条鱼的湖中抽样"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "array([2, 3, 4, 5, 6])"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fish_5 = np.array([2, 3, 4, 5, 6])\n",
    "fish_5"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:09.592780Z",
     "end_time": "2024-04-16T13:21:09.601196Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "array([3])"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从总体中随机抽样\n",
    "np.random.choice(fish_5, size=1, replace=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:09.604195Z",
     "end_time": "2024-04-16T13:21:09.607916Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "array([5, 6, 2])"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从总体中随机抽样\n",
    "np.random.choice(fish_5, size=3, replace=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:09.608918Z",
     "end_time": "2024-04-16T13:21:09.614910Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "array([6, 5, 4])"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.choice(fish_5, size=3, replace=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:09.614910Z",
     "end_time": "2024-04-16T13:21:09.621375Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "array([4, 3, 6])"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 设定随机数种子以得到相同结果\n",
    "np.random.seed(1)\n",
    "np.random.choice(fish_5, size=3, replace=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:09.623376Z",
     "end_time": "2024-04-16T13:21:09.650743Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "array([4, 3, 6])"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "np.random.choice(fish_5, size=3, replace=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:09.628215Z",
     "end_time": "2024-04-16T13:21:09.650743Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "4.333"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算样本均值\n",
    "np.random.seed(1)\n",
    "np.mean(np.random.choice(fish_5, size=3, replace=False))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:09.637446Z",
     "end_time": "2024-04-16T13:21:09.651745Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 6. 从鱼较多的湖中抽样"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "0    5.297442\n1    3.505566\n2    3.572546\n3    3.135979\n4    4.689275\nName: length, dtype: float64"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 鱼较多的总体\n",
    "fish_100000 = pd.read_csv(\"3-4-1-fish_length_100000.csv\")[\"length\"]\n",
    "fish_100000.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:09.642742Z",
     "end_time": "2024-04-16T13:21:09.672237Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "100000"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fish_100000)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:09.674238Z",
     "end_time": "2024-04-16T13:21:09.679236Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "array([4.091, 5.465, 3.426, 4.287, 4.244, 4.282, 4.29 , 5.087, 2.769,\n       5.296])"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 抽样模拟实验\n",
    "sampling_result = np.random.choice(fish_100000, size=10, replace=False)\n",
    "sampling_result"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:09.681235Z",
     "end_time": "2024-04-16T13:21:09.725806Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "4.324"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 样本均值\n",
    "np.mean(sampling_result)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:09.692156Z",
     "end_time": "2024-04-16T13:21:09.759814Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 7. 总体分布"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "4.000"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(fish_100000)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:09.696665Z",
     "end_time": "2024-04-16T13:21:09.760814Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "0.800"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(fish_100000, ddof=0)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:09.704142Z",
     "end_time": "2024-04-16T13:21:09.760814Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "0.640"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(fish_100000, ddof=0)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:09.711979Z",
     "end_time": "2024-04-16T13:21:09.760814Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='length', ylabel='Count'>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(fish_100000, kde=False, color='black')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:09.716806Z",
     "end_time": "2024-04-16T13:21:10.091563Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 8. 对比总体分布和正态分布的概率密度函数"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "array([1. , 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2,\n       2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5,\n       3.6, 3.7, 3.8, 3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8,\n       4.9, 5. , 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6. , 6.1,\n       6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, 7. ])"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(start=1, stop=7.1, step=0.1)\n",
    "x"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:10.092563Z",
     "end_time": "2024-04-16T13:21:10.097237Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "array([4.407e-04, 6.988e-04, 1.091e-03, 1.676e-03, 2.536e-03, 3.778e-03,\n       5.540e-03, 7.998e-03, 1.137e-02, 1.591e-02, 2.191e-02, 2.971e-02,\n       3.967e-02, 5.215e-02, 6.749e-02, 8.598e-02, 1.078e-01, 1.332e-01,\n       1.619e-01, 1.938e-01, 2.283e-01, 2.648e-01, 3.025e-01, 3.401e-01,\n       3.764e-01, 4.102e-01, 4.401e-01, 4.648e-01, 4.833e-01, 4.948e-01,\n       4.987e-01, 4.948e-01, 4.833e-01, 4.648e-01, 4.401e-01, 4.102e-01,\n       3.764e-01, 3.401e-01, 3.025e-01, 2.648e-01, 2.283e-01, 1.938e-01,\n       1.619e-01, 1.332e-01, 1.078e-01, 8.598e-02, 6.749e-02, 5.215e-02,\n       3.967e-02, 2.971e-02, 2.191e-02, 1.591e-02, 1.137e-02, 7.998e-03,\n       5.540e-03, 3.778e-03, 2.536e-03, 1.676e-03, 1.091e-03, 6.988e-04,\n       4.407e-04])"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.norm.pdf(x=x, loc=4, scale=0.8)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:10.099238Z",
     "end_time": "2024-04-16T13:21:10.104974Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x23939cb6010>]"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, stats.norm.pdf(x=x, loc=4, scale=0.8), color='black')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:10.106975Z",
     "end_time": "2024-04-16T13:21:10.263478Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\AppData\\Local\\Temp\\ipykernel_25336\\664535107.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(fish_100000, kde=False, norm_hist=True, color='black')\n"
     ]
    },
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x23939af6010>]"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 把正态分布的概率密度和总体的直方图重合\n",
    "sns.distplot(fish_100000, kde=False, norm_hist=True, color='black')\n",
    "plt.plot(x, stats.norm.pdf(x=x, loc=4, scale=0.8), color='black')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:10.263478Z",
     "end_time": "2024-04-16T13:21:10.486555Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 9. 抽样过程的抽象描述"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "array([4.037, 3.498, 4.322, 5.295, 5.499, 2.905, 4.437, 4.665, 3.786,\n       4.569])"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampling_norm = stats.norm.rvs(loc=4, scale=0.8, size=10)\n",
    "sampling_norm"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:10.488549Z",
     "end_time": "2024-04-16T13:21:10.493362Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "4.301"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 样本均值\n",
    "np.mean(sampling_norm)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:10.495363Z",
     "end_time": "2024-04-16T13:21:10.498952Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2024-04-16T13:21:10.498952Z",
     "end_time": "2024-04-16T13:21:10.503829Z"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
